Advances in Data Mining. Applications and Theoretical by Hongmin Cai (auth.), Petra Perner (eds.)

By Hongmin Cai (auth.), Petra Perner (eds.)

This publication constitutes the refereed complaints of the eleventh business convention on info Mining, ICDM 2011, held in long island, united states in September 2011.

The 22 revised complete papers offered have been conscientiously reviewed and chosen from a hundred submissions. The papers are equipped in topical sections on information mining in drugs and agriculture, information mining in advertising and marketing, information mining for business methods and in telecommunication, Multimedia facts Mining, theoretical elements of knowledge mining, information Warehousing, WebMining and knowledge Mining.

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484–488 (2006) 4. : Shrinking the tube: A new support vector regression algorithm. Advances in Neural Information Processing Systems (1999) 5. : Support vector method for novelty detection. Advances in Neural Information Processing Systems 12, 582–588 (2000) 6. : Clustering via Minimum Volume Ellipsoids. Journal of Comp. Optimization and App. 37(3) (2007) 7. : A survey of multidimensional medians. International Statistical Review 58(3), 263–277 (1990) 8. : Support vector domain description. Pattern Recognition Letters 20(11-13), 1191–1199 (1999) 9.

Then the tree is extended by two further nodes. To each node the subset created by splitting based on the attribute values is assigned and the tree building process is repeated. Decision tree induction is a supervised method. It requires that the data is labeled by its class. The induced decision tree tends to overfit the data. In Figure 3 we have demonstrated this situation based on a tree induced based on the well-known IRIS data set. Overfit is typically due to noise in the attribute values and class information present in the training set.

40–55, 2011. © Springer-Verlag Berlin Heidelberg 2011 How to Interpret Decision Trees? 41 The most preferred method among the methods with explanation capability is decision tree induction method [1]. This method can easily learn a decision tree without heavy user interaction while in neural nets a lot of time is spent on training the net. Cross-validation methods can be applied to decision tree induction methods; these methods ensure that the calculated error rate comes close to the true error rate.

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